Disturbance estimation using sliding mode for discrete Kalman filter

An approach to estimating disturbances using a sliding mode for an optimal Kalman filter is presented. We show that the disturbance estimation problem can be converted into a discrete tracking problem by using the difference of time update and measurement update of the Kalman filter. Disturbances cannot be modeled or measured previously, so the robustness of the sliding mode provides a very effective tool for disturbance estimation. We develop and improve a disturbance estimation algorithm using a discrete sliding mode for a discrete Kalman filter. The suggested algorithm can be easily implemented on real time applications